Key Responsibilities and Required Skills for Analytics Manager

🎯 Role Definition

The Analytics Manager leads a team of analysts and BI professionals to turn raw data into actionable insights that drive product, marketing, sales, and executive decision-making. This role owns analytics strategy, prioritization, delivery of dashboards and models, governance of metrics and measurement frameworks, and close partnership with cross-functional stakeholders to ensure data-driven outcomes across the organization. The Analytics Manager balances hands-on analysis with people leadership, project management, and technical stewardship of analytics tools and pipelines.

📈 Career Progression

Typical Career Path

Entry Point From:

Senior Data Analyst with demonstrated domain expertise and stakeholder ownership
Business Intelligence Lead experienced in dashboarding, reporting and tool administration
Product Analyst or Revenue/Marketing Analyst who has led cross-functional analytics projects

Advancement To:

Director of Analytics / Director of Business Intelligence
Head of Data & Insights / Head of Analytics
VP of Data, Analytics, or Product Analytics
Chief Data Officer (for enterprise roles with cross-functional remit)

Lateral Moves:

Product Manager or Product Strategy (analytics-driven product roles)
Data Engineering Manager (if transitioning toward infrastructure and pipelines)
Insights & Strategy Manager or Operations Strategy roles

Core Responsibilities

Primary Functions

Lead, mentor and grow a high-performing analytics team, including hiring, performance reviews, career development plans and creating a culture of rigorous analytical thinking and cross-functional partnership.
Define and drive the analytics strategy and roadmap for the organization, aligning analytics priorities with business goals, product roadmaps, and executive KPIs.
Establish and maintain standardized definitions of key business metrics (e.g., ARR, LTV, CAC, MAU/DAU, churn) and ensure consistent, auditable metric governance across reporting systems and dashboards.
Partner closely with product, marketing, finance and operations leaders to translate business questions into analytics requirements, prioritize work and deliver measurable outcomes that influence decision-making.
Own the design, development and delivery of executive-level dashboards and self-service BI tooling (Tableau, Power BI, Looker) that provide timely and accurate insights for leadership.
Oversee advanced analytics projects including segmentation, forecasting, propensity modeling, lifetime value modeling and predictive churn models to inform retention and monetization strategies.
Lead A/B testing strategy and experimentation programs: design experiments, validate statistical significance, interpret results, and partner with product teams to operationalize learnings.
Perform deep-dive analyses to diagnose business issues (e.g., conversion funnel leaks, pricing sensitivity, campaign performance) and present recommendations with clear impact estimates and implementation plans.
Build and manage robust analytics pipelines and ETL/ELT processes in collaboration with data engineering to ensure reliable, timely, and documented data flows from source systems to analytical stores (Snowflake, Redshift, BigQuery).
Implement and enforce data quality monitoring, anomaly detection, and reconciliation processes, establishing ownership and SLAs for reliable reporting.
Manage vendor relationships and tool selection for analytics and BI platforms, including evaluating prospective partners, negotiating contracts and ensuring integrations align with technical and business needs.
Champion adoption of self-service analytics by designing scalable data models, semantic layers and governance policies that enable non-technical stakeholders to access insights safely.
Translate complex data findings into concise, persuasive presentations for senior leadership and cross-functional teams using clear stories, supporting visualizations and action-focused recommendations.
Allocate and manage team resources across multiple analytics initiatives, balancing short-term requests with long-term strategic projects and maintaining a prioritized analytics backlog.
Create measurement frameworks for product launches, marketing campaigns, and strategic initiatives to ensure impact can be quantified, tracked and iterated upon.
Collaborate with legal, security and privacy teams to ensure analytics practices comply with data privacy laws, company policies and regulatory requirements (GDPR, CCPA).
Drive cross-functional analytics initiatives such as pricing optimization, customer segmentation, ROI measurement and lifecycle analytics to unlock growth and retention opportunities.
Establish performance metrics for the analytics function itself (time-to-insight, dashboard adoption, predictive accuracy) and report on team impact to leadership.
Facilitate knowledge-sharing, best practices, standardized templates and reproducible analysis methods across the analytics organization to increase quality and efficiency.
Act as the escalation point for complex ad-hoc analysis, data discrepancies and stakeholder disputes about metric definitions, ensuring timely resolution and clear communication.
Lead the roadmap for instrumentation and event tracking (product telemetry, analytics events), partnering with product and engineering to ensure accurate capture of user behavior and product usage data.
Coordinate cross-team analytics sprints and agile processes, define deliverables, set realistic milestones and remove blockers to ensure timely completion of analytics projects.

Secondary Functions

Support ad-hoc data requests and exploratory data analysis.
Contribute to the organization's data strategy and roadmap.
Collaborate with business units to translate data needs into engineering requirements.
Participate in sprint planning and agile ceremonies within the data engineering team.
Provide training and documentation for business users to interpret dashboards and standardized reports.
Assist Finance with modeling and forecasting for budgeting, planning and scenario analysis.
Represent analytics in cross-functional forums to align on priorities and promote data literacy.
Help define retention, onboarding and activation metrics and recommend product interventions.

Required Skills & Competencies

Hard Skills (Technical)

Advanced SQL proficiency for querying, aggregating and modeling large datasets across production data warehouses (Snowflake, Redshift, BigQuery).
Hands-on experience with BI and dashboarding tools such as Tableau, Power BI, Looker, Mode or similar platforms to build executive dashboards and self-service reporting layers.
Strong statistical and quantitative skills, including hypothesis testing, regression analysis, forecasting, uplift modeling and causal inference techniques.
Practical experience designing and analyzing A/B tests and experimentation frameworks.
Proficiency in a scripting language for analysis and modeling (Python or R), including libraries for data manipulation (pandas, dplyr), visualization and basic machine learning (scikit-learn, caret).
Familiarity with ETL/ELT tools and pipelines (Airflow, dbt, Fivetran, Stitch) and data modeling best practices (star schemas, dimensional modeling).
Experience with cloud data platforms and storage, and an understanding of data governance, lineage and metadata management.
Knowledge of product analytics instrumentation, event tracking (Segment, Amplitude, Mixpanel) and analytics SDK integration.
Ability to translate business requirements into technical specifications for data engineering and analytics implementation.
Experience with forecasting, cohort analysis, customer lifetime value modeling and revenue/financial analytics.
Familiarity with machine learning lifecycle basics and deployment considerations for production analytics use cases.

Soft Skills

Strong stakeholder management and business partnership skills, able to influence senior leaders and translate analytical results into business impact.
Clear and persuasive communication and data storytelling skills—comfortable presenting to executives and non-technical audiences.
Leadership and people management capabilities including coaching, performance feedback, career development and hiring.
Strategic thinking with a bias for actionable results and outcome-oriented roadmaps.
Excellent project management, prioritization, and time management skills in a fast-paced environment.
High attention to detail, strong critical thinking and problem-solving orientation.
Adaptability and curiosity to learn new tools, techniques and business domains.
Collaboration and facilitation skills to align cross-functional teams on analytics priorities and measurement approaches.
Ethical mindset around data privacy, security, and responsible use of analytics.
Strong business acumen and the ability to contextualize analytics within broader commercial goals.

Education & Experience

Educational Background

Minimum Education:

Bachelor's degree in a quantitative, technical or business discipline such as Statistics, Mathematics, Economics, Computer Science, Data Science, Business Analytics or Engineering.

Preferred Education:

Master's degree or MBA in Analytics, Data Science, Statistics, Economics, or a related technical/business field.

Relevant Fields of Study:

Data Science / Analytics
Statistics / Mathematics
Economics / Econometrics
Computer Science / Software Engineering
Business / Finance / Marketing Analytics

Experience Requirements

Typical Experience Range:

5–10+ years in analytics, business intelligence, or data science roles, with progressive responsibility.

Preferred:

7+ years of analytics experience with 2+ years managing or leading an analytics team, demonstrated success delivering analytics products (dashboards, models, and experimentation) that drove measurable business outcomes.
Experience working in fast-growth technology companies or cross-functional enterprise environments and managing analytics in cloud-based data platforms.